Background: Cadmium (Cd) is a toxic metal associated with increased morbidity and mortality. Urinary Cd (U-Cd) concentration is considered a biomarker of long-term exposure.Objectives: Our objectives were to evaluate the within-person correlation among repeat samples and to identify predictors of U-Cd concentrations.Methods: U-Cd concentrations (micrograms per liter) were measured in 24-hr urine samples collected from 296 women enrolled in the California Teachers Study in 2000 and a second 24-hr sample collected 3–9 months later from 141 of the participants. Lifestyle and sociodemographic characteristics were obtained via questionnaires. The Total Diet Study database was used to quantify dietary cadmium intake based on a food frequency questionnaire. We estimated environmental cadmium emissions near participants’ residences using a geographic information system.Results: The geometric mean U-Cd concentration was 0.27 µg/L and the range was 0.1–3.6 µg/L. The intraclass correlation among repeat samples from an individual was 0.50. The use of a single 24-hr urine specimen to characterize Cd exposure in a case–control study would result in an observed odds ratio of 1.4 for a true odds ratio of 2.0. U-Cd concentration increased with creatinine, age, and lifetime pack-years of smoking among ever smokers or lifetime intensity-years of passive smoking among nonsmokers, whereas it decreased with greater alcohol consumption and number of previous pregnancies. These factors explained 42–44% of the variability in U-Cd concentrations.Conclusion: U-Cd levels varied with several individual characteristics, and a single measurement of U-Cd in a 24-hr sample did not accurately reflect medium- to long-term body burden.

words; limit is 250)Background: Large-scale cancer epidemiology cohorts (CECs) have successfully collected, analyzed, and shared patient-reported data for years. CECs increasingly need to make their data more Findable, Accessible, Interoperable, and Reusable, or FAIR. How CECs should approach this transformation is unclear. Methods:The California Teachers Study (CTS) is an observational CEC of 133,477 participants followed since 1995-1996. In 2014, we began updating our data storage, management, analysis, and sharing strategy. With the San Diego Supercomputer Center, we deployed a new infrastructure based on a Data Warehouse, to integrate and manage data; and a secure and shared workspace with documentation, software, and analytic tools that facilitate collaboration and accelerate analyses. Results: Our new CTS infrastructure includes a Data Warehouse and data marts, which are focused subsets from the Data Warehouse designed for efficiency. The secure CTS workspace utilizes a Remote Desktop service that operates within a HIPAA and FISMA compliant platform. Our infrastructure offers broad access to CTS data; includes statistical analysis and data visualization software and tools; flexibly managesother key data activities (e.g., cleaning, updates, & data sharing); and will continue to evolve to advance FAIR principles.on July 15, 2020. Conclusion:Our scalable infrastructure provides the security, authorization, data model, metadata, and analytic tools needed to manage, share, and analyze CTS data in ways that are consistent with the NCI's CancerResearch Data Commons Framework. Impact:The CTS's implementation of new infrastructure in an ongoing CEC demonstrates how population sciences can explore and embrace new cloud-based and analytics infrastructure to accelerate cancer research and translation.on July 15, 2020.

PurposeCancer of the prostate (CaP) is the leading cancer among men in sub-Saharan Africa (SSA). A substantial proportion of these men with CaP are diagnosed at late (usually incurable) stages, yet little is known about the etiology of CaP in SSA.MethodsWe established the Men of African Descent and Carcinoma of the Prostate Network, which includes seven SSA centers partnering with five US centers to study the genetics and epidemiology of CaP in SSA. We developed common data elements and instruments, regulatory infrastructure, and biosample collection, processing, and shipping protocols. We tested this infrastructure by collecting epidemiologic, medical record, and genomic data from a total of 311 patients with CaP and 218 matched controls recruited at the seven SSA centers. We extracted genomic DNA from whole blood, buffy coat, or buccal swabs from 265 participants and shipped it to the Center for Inherited Disease Research (Baltimore, MD) and the Centre for Proteomics and Genomics Research (Cape Town, South Africa), where genotypes were generated using the UK Biobank Axiom Array.ResultsWe used common instruments for data collection and entered data into the shared database. Double-entered data from pilot participants showed a 95% to 98% concordance rate, suggesting that data can be collected, entered, and stored with a high degree of accuracy. Genotypes were obtained from 95% of tested DNA samples (100% from blood-derived DNA samples) with high concordance across laboratories.ConclusionWe provide approaches that can produce high-quality epidemiologic and genomic data in multicenter studies of cancer in SSA.

No abstract